By: T-Mobile

May 14, 2020
You’ve built a model that stakeholders love. Now, you need it to retrain every day… and that requires a whole new architecture. In this article, Mark Eric Hanson outlines why automated ML model retraining is important, the use cases it solves for T-Mobile, and how to stitch the tech together to recreate a similar implementation of your own. Its a fascinating read, and a good behind-the-scenes look at how we approach problem-solving at T-Mobile.
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May 01, 2020
Hi all, I’m Joe Searcy, Member of Technical Staff on the Platform Engineering team here at T-Mobile and I’m joined by Torin Sandall, Software Engineer at Styra and Co-founder/Core Contributor for the Open Policy Agent (OPA) project, to help tell the story behind one of T-Mobile’s latest open source projects, MagTape. History & Motivation Here at T-Mobile we’re ALL-IN on containers and container platforms like Kubernetes. We, like most companies building Kubernetes environments, came to a quick realization that empowering your developers with platforms like Kubernetes can exponentially increase the volume and velocity of applications and releases.
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January 01, 0001
Kubernetes (R) has become the Container orchestration platform of choice–but launching and configuring a Production grade Kubernetes Cluster is a lot of work. Conducktor-Go simplifies launching native Kubernetes Clusters on AWS (R) using Terraform (TM), Ansible (R), and Python Scripts. It also provides a rich, turn-key telemetry solution with pre-configured Grafana(R) dashboards and Prometheus(R) as data source. Conducktor-GO also configures Nginx(R), Docker(R) CE, Weave Net (CNI), Kube2IAM, Kubernetes 2.0 Dashboard, Kube-State-Metrics, and Metrics Server to provide a Production ready Kubernetes Cluster to deploy your services.
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